Damage precursor based structural health monitoring and damage prognosis framework

Rabiei, Elaheh; Droguett, Enrique López; Modarres, Mohammad; Amiri, Mohammad Javad Taheri

doi:10.1201/b19094-319

Cited by 8 publications

(13 citation statements)

References 2 publications

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“…The interest in extending the original PF to augmented PF was aroused recently, in order to estimate both the process model parameters and states simultaneously [ 23 , 26 , 27 , 40 , 41 ]. This is particularly useful when dealing with uncertain dynamic systems in which all or some of the process model parameters might be unknown.…”

Section: Augmented Particle Filteringmentioning

confidence: 99%

“…Its popularity has increased rapidly in the reliability field and various versions, such as auxiliary PF [ 1 ], regularized PF [ 2 ] and unscented PF [ 3 ], are proposed to improve the performance of the standard particle filtering algorithm. The standard PF algorithm and its variants have been implemented for diagnostics and prognostics in a wide range of applications, including life prediction of batteries and fuel cells [ 4 , 5 , 6 , 7 ], degradation assessment and prediction in gears and bearings [ 8 , 9 , 10 ], health monitoring and prognostics of gas turbines [ 11 ], machine tools [ 12 ], and pumps [ 13 , 14 ], and also, in damage estimation and prediction of composite materials [ 15 , 16 , 17 , 18 ]. More application examples can be found in a recent review paper on the PF algorithm by Jouin et al [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Some examples can be found in [ 4 , 11 , 32 , 33 , 34 ]. This leads to a very basic and simple linear measurement model, as follows:

The second group can be considered as the cases when “

is NOT necessarily the same quantity as

”; for example in [ 16 , 35 , 36 , 37 ]. Therefore,

has a different nature and needs to be related to the hidden variable through some physical or data-driven model:

…”

Section: Introductionmentioning

confidence: 99%

“…The second group can be considered as the cases when “

is NOT necessarily the same quantity as

”; for example in [ 16 , 35 , 36 , 37 ]. Therefore,

has a different nature and needs to be related to the hidden variable through some physical or data-driven model:

…”

Section: Introductionmentioning

confidence: 99%

“…This research article is part of the Ph.D. dissertation of the first author, Elaheh Rabiei [ 41 ]. She conceived of the presented idea and developed the theory and performed the computations.…”

mentioning

confidence: 99%

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Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis

Rabiei

Droguett

Modarres

2018

Entropy

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A fully adaptive particle filtering algorithm is proposed in this paper which is capable of updating both state process models and measurement models separately and simultaneously. The approach is a significant step toward more realistic online monitoring or tracking damage. The majority of the existing methods for Bayes filtering are based on predefined and fixed state process and measurement models. Simultaneous estimation of both state and model parameters has gained attention in recent literature. Some works have been done on updating the state process model. However, not many studies exist regarding an update of the measurement model. In most of the real-world applications, the correlation between measurements and the hidden state of damage is not defined in advance and, therefore, presuming an offline fixed measurement model is not promising. The proposed approach is based on optimizing relative entropy or Kullback-Leibler divergence through a particle filtering algorithm. The proposed algorithm is successfully applied to a case study of online fatigue damage estimation in composite materials.

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